ArticleJournal of molecular modeling2020
An adaptive design approach for defects distribution modeling in materials from first-principle calculations.
Article in Journal of molecular modeling, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Interpretable machine learning for optimized dimethyl ether production from bio-methanol.Scientific reports · 2026Article
- A new active learning approach for adsorbate-substrate structural elucidation in silico.Journal of molecular modeling · 2022Article
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Authors and funding
5 authors.
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Abstract
Designing and understanding the mechanism of non-stoichiometric materials with enhanced properties is challenging, both experimentally and even computationally, due to the large number of chemical spaces and their distributions through the material. In the current work, it is proposed a Machine Learning approach coupled with the Efficient Global Optimization (EGO) method-an Adaptive Design (AD)-to model local defects in materials from first-principle calculations. Our method takes into account the smallest sample set as possible, envisioning the material defect structure relationship with target properties for new insights. As an example, the AD framework allows us to study the stability and the structure of the modified goethite (Fe
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